The zelig command estimates a variety of statistical
models. Use zelig output with setx and sim to compute
quantities of interest, such as predicted probabilities, expected values, and
first differences, along with the associated measures of uncertainty
(standard errors and confidence intervals).
Usage
zelig(formula, model, data, ..., by = NULL, cite = TRUE)
Arguments
formula
a symbolic representation of the model to be
estimated, in the form y ~, x1 + x2, where y is the
dependent variable and x1 and x2 are the explanatory
variables, and y, x1, and x2 are contained in the
same dataset. (You may include more than two explanatory variables,
of course.) The + symbol means “inclusion” not
“addition.” You may also include interaction terms and main
effects in the form x1*x2 without computing them in prior
steps; I(x1*x2) to include only the interaction term and
exclude the main effects; and quadratic terms in the form
I(x1^2)
model
the name of a statistical model.
Type help.zelig("models") to see a list of currently supported
models
data
the name of a data frame containing the variables
referenced in the formula, or a list of multiply imputed data frames
each having the same variable names and row numbers (created by
mi)
...
additional arguments passed to zelig,
depending on the model to be estimated
by
a factor variable contained in data. Zelig will subset
the data frame based on the levels in the by variable, and
estimate a model for each subset. This a particularly powerful option
which will allow you to save a considerable amount of effort. For
example, to run the same model on all fifty states, you could type:
z.out <- zelig(y ~ x1 + x2, data = mydata, model = "ls", by = "state")
You may also use by to run models using MatchIt subclass
cite
If is set to "TRUE" (default), the model citation will be
Value
Depending on the class of model selected, zelig will return
an object with elements including coefficients, residuals,
and formula which may be summarized using
summary(z.out) or individually extracted using, for example,
z.out$coefficients. See the specific models listed above
for additional output values, or simply type names(z.out).
providing 
.